Re-ranking is the control point between retrieval and generation, yet many teams rely on default ordering that hides precision trade-offs and limits quality gains. This workshop dives deep into how re-ranking frameworks, scoring strategies, and evaluation methods shape RAG performance.
To win, your GenAI solutions must apply deliberate re-ranking strategies that balance recall and precision while measurably improving generation quality.
When re-ranking is treated as an afterthought, teams face recurring problems:
- Fragile RAG pipelines: Retrieval results are passed directly into generation without a clear re-ranking framework or control strategy.
• Unclear relevance signals: Retrieved passages are weakly scored, making it difficult to prioritize the most useful context.
• Unmeasured trade-offs: Teams cannot confidently balance recall versus precision or validate improvements experimentally.
These issues lead to wasted context windows, inconsistent outputs, and slow progress toward reliable RAG systems.
In this hands-on workshop, your team designs, applies, and evaluates RAG re-ranking techniques through structured exercises and guided analysis.
- Design RAG re-ranking frameworks that sit cleanly between retrieval and generation.
• Implement relevance scoring approaches to rank retrieved passages effectively.
• Optimize generation inputs by selecting and ordering context through ranking strategies.
• Evaluate recall versus precision trade-offs introduced by different re-ranking choices.
• Test re-ranking models using A/B experiments to validate impact on generation quality.
Designing RAG Re-Ranking Frameworks
Scoring Retrieved Passages by Relevance
Optimizing Generation Inputs via Ranking
Evaluating Trade-offs Between Recall and Precision
Testing Re-Ranking Models with A/B Experiments
• Design structured re-ranking frameworks for RAG pipelines.
• Apply relevance scoring to improve passage prioritization.
• Optimize generation inputs through deliberate ranking decisions.
• Evaluate recall and precision trade-offs with clear criteria.
• Validate re-ranking improvements using controlled A/B testing.
Who Should Attend:
Solution Essentials
Virtual or in-person
4 hours
Intermediate
RAG pipelines, re-ranking models, evaluation notebooks, and experiment frameworks